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stochastic simulation: algorithms and analysis  (Verlag GmbH)

 
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    Verlag GmbH stochastic simulation: algorithms and analysis
    Stochastic Simulation: Algorithms And Analysis, supplied by Verlag GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stochastic+simulation/stochastic+simulation++algorithms+and+analysis/pm30774112-365-1-6
    Average 90 stars, based on 1 article reviews
    stochastic simulation: algorithms and analysis - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    other:

    Article Title: Designing Hyperchaos and Intermittency in Semiconductor Superlattices
    Article Snippet: Glynn, Stochastic Simulation: Algorithms and Analysis (Springer-Verlag, Berlin, 2007).

    Article Title: Ultrahigh-Speed Random Number Generation Based on a Chaotic Semiconductor Laser
    Article Snippet: The fluctuating intensity of a chaotic semiconductor laser is used for generating random sequences at rates up to 12:5 Gbits=s.. The conversion of the fluctuating intensity to a random bit sequence can be implemented in either software or hardware and the overall rate of generation is much faster than any previously reported random number generator based on a physical mechanism.. The generator’s simplicity, robustness, and insensitivity to control parameters should enable its application to tasks of secure communication and calculation procedures requiring ultrahigh-speed generation of random bit sequences.

    Article Title: Efficient anticorrelated variance reduction for stochastic simulation of biochemical reactions.
    Article Snippet: : ‘Stochastic simulation: algorithms and analysis’ (Springer-Verlag, New York, USA, 2007) [37] Robert, C., Casella, G.: ‘Monte Carlo statistical methods’ (Springer-Verlag, New York, USA, 2004, 2nd edn.)



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    (A) Pulsing (blue) and constant (orange) 0A~P signal used as input to the biofilm matrix production <t>stochastic</t> model. Pulsing signal corresponds to the signal for growth rate equal to 0.4 h −1 predicted by the phosphorelay network model. The constant 0A~P signal corresponds to the mean of the pulsing signal. (B) Stochastic simulations starting from biofilm matrix inactive state for both constant and pulsing 0A~P signal. Matrix production is considered inactive if the number of TapA molecules < 200 (dashed line). (C) Stochastic simulations starting from matrix active state for both constant and pulsing 0A~P signal. Matrix production is considered active if the number of TapA molecules ≥ 200 (dashed line). (D) Fraction of active cells as a function of time for constant (dark orange) and pulsing 0A~P signal (dark blue) estimated from fitting and to the stochastic simulation data of ‘ Initially OFF cells ’ (light blue) and ‘ Initially ON’ (light orange) cells, respectively. All fits have R 2 >= 0.77 and MSE < 0.005. A total of 2000 simulations were performed. (inset) Estimated values of the biofilm activation rate ( k ON ) and of the matrix deactivation rate assuming pulsing and constant 0A~P input .
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    Image Search Results


    (A) Pulsing (blue) and constant (orange) 0A~P signal used as input to the biofilm matrix production stochastic model. Pulsing signal corresponds to the signal for growth rate equal to 0.4 h −1 predicted by the phosphorelay network model. The constant 0A~P signal corresponds to the mean of the pulsing signal. (B) Stochastic simulations starting from biofilm matrix inactive state for both constant and pulsing 0A~P signal. Matrix production is considered inactive if the number of TapA molecules < 200 (dashed line). (C) Stochastic simulations starting from matrix active state for both constant and pulsing 0A~P signal. Matrix production is considered active if the number of TapA molecules ≥ 200 (dashed line). (D) Fraction of active cells as a function of time for constant (dark orange) and pulsing 0A~P signal (dark blue) estimated from fitting and to the stochastic simulation data of ‘ Initially OFF cells ’ (light blue) and ‘ Initially ON’ (light orange) cells, respectively. All fits have R 2 >= 0.77 and MSE < 0.005. A total of 2000 simulations were performed. (inset) Estimated values of the biofilm activation rate ( k ON ) and of the matrix deactivation rate assuming pulsing and constant 0A~P input .

    Journal: bioRxiv

    Article Title: Changes in Spo0A~P pulsing frequency control biofilm matrix deactivation

    doi: 10.1101/2025.02.13.638117

    Figure Lengend Snippet: (A) Pulsing (blue) and constant (orange) 0A~P signal used as input to the biofilm matrix production stochastic model. Pulsing signal corresponds to the signal for growth rate equal to 0.4 h −1 predicted by the phosphorelay network model. The constant 0A~P signal corresponds to the mean of the pulsing signal. (B) Stochastic simulations starting from biofilm matrix inactive state for both constant and pulsing 0A~P signal. Matrix production is considered inactive if the number of TapA molecules < 200 (dashed line). (C) Stochastic simulations starting from matrix active state for both constant and pulsing 0A~P signal. Matrix production is considered active if the number of TapA molecules ≥ 200 (dashed line). (D) Fraction of active cells as a function of time for constant (dark orange) and pulsing 0A~P signal (dark blue) estimated from fitting and to the stochastic simulation data of ‘ Initially OFF cells ’ (light blue) and ‘ Initially ON’ (light orange) cells, respectively. All fits have R 2 >= 0.77 and MSE < 0.005. A total of 2000 simulations were performed. (inset) Estimated values of the biofilm activation rate ( k ON ) and of the matrix deactivation rate assuming pulsing and constant 0A~P input .

    Article Snippet: The stochastic model was implemented in MATLAB R2023b, and the stochastic simulations ( ) were done using the SimBiology tool of MATLAB R2023b.

    Techniques: Activation Assay

    Histogram of TapA molecules obtained from 3000 stochastic simulations (blue bars). Simulation time was set to 50 h. Solid lines represent Gaussian fits derived from a Gaussian Mixture Model with 2 components fitted to the data. The value of 200 molecules was set to be the threshold to distinguish between a biofilm matrix production active and inactive.

    Journal: bioRxiv

    Article Title: Changes in Spo0A~P pulsing frequency control biofilm matrix deactivation

    doi: 10.1101/2025.02.13.638117

    Figure Lengend Snippet: Histogram of TapA molecules obtained from 3000 stochastic simulations (blue bars). Simulation time was set to 50 h. Solid lines represent Gaussian fits derived from a Gaussian Mixture Model with 2 components fitted to the data. The value of 200 molecules was set to be the threshold to distinguish between a biofilm matrix production active and inactive.

    Article Snippet: The stochastic model was implemented in MATLAB R2023b, and the stochastic simulations ( ) were done using the SimBiology tool of MATLAB R2023b.

    Techniques: Derivative Assay

    Journal: bioRxiv

    Article Title: Changes in Spo0A~P pulsing frequency control biofilm matrix deactivation

    doi: 10.1101/2025.02.13.638117

    Figure Lengend Snippet:

    Article Snippet: The stochastic model was implemented in MATLAB R2023b, and the stochastic simulations ( ) were done using the SimBiology tool of MATLAB R2023b.

    Techniques: